OpenRaiser/Intern-Atlas huggingface dataset:
Applications Of Zero-Knowledge Proofs On Bitcoin
Venue: arXiv cs.CR 2025
Year: 2025

This paper explores how zero-knowledge proofs can enhance Bitcoin's functionality and privacy. First, we consider Proof-of-Reserve schemes: by using zk-STARKs, a custodian can prove its Bitcoin holdings are more than a predefined threshold X, without revealing addresses or actual balances. We outline a STARK-based protocol for Bitcoin UTXOs and discuss its efficiency. Second, we examine ZK Light Clients, where a mobile or lightweight device verifies Bitcoin's proof-of-work chain using succinct proofs. We propose a protocol for generating and verifying a STARK-based proof of a chain of block headers, enabling trust-minimized client operation. Third, we explore Privacy-Preserving Rollups via BitVM: leveraging BitVM, we design a conceptual rollup that keeps transaction data confidential using zero-knowledge proofs. In each case, we analyze security, compare with existing approaches, and discuss implementation considerations. Our contributions include the design of concrete protocols adapted to Bitcoin's UTXO model and an assessment of their practicality. The results suggest that while ZK proofs can bring powerful features (e.g., on-chain reserve audits, trustless light clients, and private layer-2 execution) to Bitcoin, each application requires careful trade-offs in efficiency and trust assumptions.
Intelligent ARP Spoofing Detection using Multi-layered Machine Learning (ML) Techniques for IoT Networks
Venue: arXiv cs.CR 2025
Year: 2025

Address Resolution Protocol (ARP) spoofing remains a critical threat to IoT networks, enabling attackers to intercept, modify, or disrupt data transmission by exploiting ARP's lack of authentication. The decentralized and resource-constrained nature of IoT environments amplifies this vulnerability, making conventional detection mechanisms ineffective at scale. This paper introduces an intelligent, multi-layered machine learning framework designed to detect ARP spoofing in real-time IoT deployments. Our approach combines feature engineering based on ARP header behavior, traffic flow analysis, and temporal packet anomalies with a hybrid detection pipeline incorporating decision trees, ensemble models, and deep learning classifiers. We propose a hierarchical architecture to prioritize lightweight models at edge gateways and deeper models at centralized nodes to balance detection accuracy and computational efficiency. The system is validated on both simulated IoT traffic and the CICIDS2017 dataset, achieving over 97% detection accuracy with low false positive rates. Comparative evaluations with signature-based and rule-based systems demonstrate the robustness and generalizability of our approach. Our results show that intelligent machine learning integration enables proactive ARP spoofing detection tailored for IoT scenarios, laying the groundwork for scalable and autonomous network security solutions.
Eliciting User Requirements for AI-Enhanced Learning Environments using a Participatory Approach
Venue: arXiv cs.HC 2025
Year: 2025

This paper explores the needs and expectations of educational stakeholders for AI (Artificial Intelligence)-enhanced learning environments. Data was collected following two-phased participatory workshops. The first workshop outlined stakeholders' profiles in terms of technical and pedagogical characteristics. The qualitative data collected was analysed using deductive thematic analysis with Activity Theory, explicating the user needs. The second workshop articulated expectations related to the integration of AI in education. Inductive thematic analysis of the second workshop led to the elicitation of users' expectations. We cross-examined the needs and expectations, identifying contradictions, to generate user requirements for emerging technologies. The paper provides suggestions for future design initiatives that incorporate AI in learning environments.
Thinking Like a Scientist: Can Interactive Simulations Foster Critical AI Literacy?
Venue: arXiv cs.HC 2025
Year: 2025

As AI systems shape individual and societal decisions, fostering critical AI literacy is essential. Traditional approaches, such as blog articles, static lessons, and social media discussions, often fail to support deep conceptual understanding and critical engagement. This study examines whether interactive simulations can help learners think like a scientist by engaging them in hypothesis testing, experimentation, and direct observation of AI behavior. In a controlled study with 605 participants, we assess how interactive AI tutorials impact learning of key concepts such as fairness, dataset representativeness, and bias in language models. Results show that interactive simulations effectively enhance AI literacy across topics, supporting greater knowledge transfer and self-reported confidence, though engagement alone does not predict learning. This work contributes to the growing field of AI literacy education, highlighting how interactive, inquiry-driven methodologies can better equip individuals to critically engage with AI in their daily lives.
SkyEye: When Your Vision Reaches Beyond IAM Boundary Scope in AWS Cloud
Venue: arXiv cs.CR 2025
Year: 2025

In recent years, cloud security has emerged as a primary concern for enterprises due to the increasing trend of migrating internal infrastructure and applications to cloud environments. This shift is driven by the desire to reduce the high costs and maintenance fees associated with traditional on-premise infrastructure. By leveraging cloud capacities such as high availability and scalability, companies can achieve greater operational efficiency and flexibility. However, this migration also introduces new security challenges. Ensuring the protection of sensitive data, maintaining compliance with regulatory requirements, and mitigating the risks of cyber threats are critical issues that must be addressed. Identity and Access Management (IAM) constitutes the critical security backbone of most cloud deployments, particularly within AWS environments. As organizations adopt AWS to scale applications and store data, the need for a thorough, methodical, and precise enumeration of IAM configurations grows exponentially. Enumeration refers to the systematic mapping and interrogation of identities, permissions, and resource authorizations with the objective of gaining situational awareness. By understanding the interplay between users, groups, and their myriads of policies, whether inline or attached managed policies, security professionals need to enumerate and identify misconfigurations, reduce the risk of unauthorized privilege escalation, and maintain robust compliance postures. This paper will present SkyEye, a cooperative multi-principal IAM enumeration framework, which comprises cutting-edge enumeration models in supporting complete situational awareness regarding the IAMs of provided AWS credentials, crossing the boundary of principal-specific IAM entitlement vision to reveal the complete visionary while insufficient authorization is the main challenge.
Creation of a Numerical Scoring System to Objectively Measure and Compare the Level of Rhetoric in Arabic Texts: A Feasibility Study, and A Working Prototype
Venue: arXiv cs.CL 2025
Year: 2025

Arabic Rhetoric is the field of Arabic linguistics which governs the art and science of conveying a message with greater beauty, impact and persuasiveness. The field is as ancient as the Arabic language itself and is found extensively in classical and contemporary Arabic poetry, free verse and prose. In practical terms, it is the intelligent use of word order, figurative speech and linguistic embellishments to enhance message delivery. Despite the volumes that have been written about it and the high status accorded to it, there is no way to objectively know whether a speaker or writer has used Arabic rhetoric in a given text, to what extent, and why. There is no objective way to compare the use of Arabic rhetoric across genres, authors or epochs. It is impossible to know which of pre-Islamic poetry, Andalucian Arabic poetry, or modern literary genres are richer in Arabic rhetoric. The aim of the current study was to devise a way to measure the density of the literary devices which constitute Arabic rhetoric in a given text, as a proxy marker for Arabic rhetoric itself. A comprehensive list of 84 of the commonest literary devices and their definitions was compiled. A system of identifying literary devices in texts was constructed. A method of calculating the density of literary devices based on the morpheme count of the text was utilised. Four electronic tools and an analogue tool were created to support the calculation of an Arabic text's rhetorical literary device density, including a website and online calculator. Additionally, a technique of reporting the distribution of literary devices used across the three sub-domains of Arabic rhetoric was created. The output of this project is a working tool which can accurately report the density of Arabic rhetoric in any Arabic text or speech.
A Formal Rebuttal of "The Blockchain Trilemma: A Formal Proof of the Inherent Trade-Offs Among Decentralization, Security, and Scalability"
Venue: arXiv cs.CR 2025
Year: 2025

This paper presents a comprehensive refutation of the so-called "blockchain trilemma," a widely cited but formally ungrounded claim asserting an inherent trade-off between decentralisation, security, and scalability in blockchain protocols. Through formal analysis, empirical evidence, and detailed critique of both methodology and terminology, we demonstrate that the trilemma rests on semantic equivocation, misuse of distributed systems theory, and a failure to define operational metrics. Particular focus is placed on the conflation of topological network analogies with protocol-level architecture, the mischaracterisation of Bitcoin's design--including the role of miners, SPV clients, and header-based verification--and the failure to ground claims in complexity-theoretic or adversarial models. By reconstructing Bitcoin as a deterministic, stateless distribution protocol governed by evidentiary trust, we show that scalability is not a trade-off but an engineering outcome. The paper concludes by identifying systemic issues in academic discourse and peer review that have allowed such fallacies to persist, and offers formal criteria for evaluating future claims in blockchain research.
TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law
Venue: arXiv cs.CL 2025
Year: 2025

As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their domain-specific safety and compliance becomes critical. While prior work has largely focused on improving LLM performance in these domains, it has often neglected the evaluation of domain-specific safety risks. To bridge this gap, we first define domain-specific safety principles for LLMs based on the AMA Principles of Medical Ethics, the ABA Model Rules of Professional Conduct, and the CFA Institute Code of Ethics. Building on this foundation, we introduce Trident-Bench, a benchmark specifically targeting LLM safety in the legal, financial, and medical domains. We evaluated 19 general-purpose and domain-specialized models on Trident-Bench and show that it effectively reveals key safety gaps -- strong generalist models (e.g., GPT, Gemini) can meet basic expectations, whereas domain-specialized models often struggle with subtle ethical nuances. This highlights an urgent need for finer-grained domain-specific safety improvements. By introducing Trident-Bench, our work provides one of the first systematic resources for studying LLM safety in law and finance, and lays the groundwork for future research aimed at reducing the safety risks of deploying LLMs in professionally regulated fields. Code and benchmark will be released at: https://github.com/zackhuiiiii/TRIDENT
Quantum Geometry of Data
Venue: arXiv cs.LG 2025
Year: 2025

We demonstrate how Quantum Cognition Machine Learning (QCML) encodes data as quantum geometry. In QCML, features of the data are represented by learned Hermitian matrices, and data points are mapped to states in Hilbert space. The quantum geometry description endows the dataset with rich geometric and topological structure - including intrinsic dimension, quantum metric, and Berry curvature - derived directly from the data. QCML captures global properties of data, while avoiding the curse of dimensionality inherent in local methods. We illustrate this on a number of synthetic and real-world examples. Quantum geometric representation of QCML could advance our understanding of cognitive phenomena within the framework of quantum cognition.
Towards Unifying Quantitative Security Benchmarking for Multi Agent Systems
Venue: arXiv cs.CR 2025
Year: 2025

Evolving AI systems increasingly deploy multi-agent architectures where autonomous agents collaborate, share information, and delegate tasks through developing protocols. This connectivity, while powerful, introduces novel security risks. One such risk is a cascading risk: a breach in one agent can cascade through the system, compromising others by exploiting inter-agent trust. In tandem with OWASP's initiative for an Agentic AI Vulnerability Scoring System we define an attack vector, Agent Cascading Injection, analogous to Agent Impact Chain and Blast Radius, operating across networks of agents. In an ACI attack, a malicious input or tool exploit injected at one agent leads to cascading compromises and amplified downstream effects across agents that trust its outputs. We formalize this attack with an adversarial goal equation and key variables (compromised agent, injected exploit, polluted observations, etc.), capturing how a localized vulnerability can escalate into system-wide failure. We then analyze ACI's properties -- propagation chains, amplification factors, and inter-agent compound effects -- and map these to OWASP's emerging Agentic AI risk categories (e.g. Impact Chain and Orchestration Exploits). Finally, we argue that ACI highlights a critical need for quantitative benchmarking frameworks to evaluate the security of agent-to-agent communication protocols. We outline a methodology for stress-testing multi-agent systems (using architectures such as Google's A2A and Anthropic's MCP) against cascading trust failures, developing upon groundwork for measurable, standardized agent-to-agent security evaluation. Our work provides the necessary apparatus for engineers to benchmark system resilience, make data-driven architectural trade-offs, and develop robust defenses against a new generation of agentic threats.
NIST Post-Quantum Cryptography Standard Algorithms Based on Quantum Random Number Generators
Venue: arXiv cs.CR 2025
Year: 2025

In recent years, the advancement of quantum computing technology has posed potential security threats to RSA cryptography and elliptic curve cryptography. In response, the National Institute of Standards and Technology (NIST) published several Federal Information Processing Standards (FIPS) of post-quantum cryptography (PQC) in August 2024, including the Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM), Module-Lattice-Based Digital Signature Algorithm (ML-DSA), and Stateless Hash-Based Digital Signature Algorithm (SLH-DSA). Although these PQC algorithms are designed to resist quantum computing attacks, they may not provide adequate security in certain specialized application scenarios. To address this issue, this study proposes quantum random number generator (QRNG)-based PQC algorithms. These algorithms leverage quantum computing to generate random numbers, which serve as the foundation for key pair generation, key encapsulation, and digital signature generation. A generalized architecture of QRNG is proposed, along with the design of six QRNGs. Each generator is evaluated according to the statistical validation procedures outlined in NIST SP 800-90B, including tests for verification of entropy sources and independent and identically distributed (IID) outputs. Experimental results assess the computation time of the six QRNGs, as well as the performance of QRNG-based ML-KEM, QRNG-based ML-DSA, and QRNG-based SLH-DSA. These findings provide valuable reference data for future deployment of PQC systems.
Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems
Venue: arXiv cs.CR 2025
Year: 2025

The rapid advancement of Generative Artificial Intelligence has fueled deepfake proliferation-synthetic media encompassing fully generated content and subtly edited authentic material-posing challenges to digital security, misinformation mitigation, and identity preservation. This systematic review evaluates state-of-the-art deepfake detection methodologies, emphasizing reproducible implementations for transparency and validation. We delineate two core paradigms: (1) detection of fully synthetic media leveraging statistical anomalies and hierarchical feature extraction, and (2) localization of manipulated regions within authentic content employing multi-modal cues such as visual artifacts and temporal inconsistencies. These approaches, spanning uni-modal and multi-modal frameworks, demonstrate notable precision and adaptability in controlled settings, effectively identifying manipulations through advanced learning techniques and cross-modal fusion. However, comprehensive assessment reveals insufficient evaluation of adversarial robustness across both paradigms. Current methods exhibit vulnerability to adversarial perturbations-subtle alterations designed to evade detection-undermining reliability in real-world adversarial contexts. This gap highlights critical disconnect between methodological development and evolving threat landscapes. To address this, we contribute a curated GitHub repository aggregating open-source implementations, enabling replication and testing. Our findings emphasize urgent need for future work prioritizing adversarial resilience, advocating scalable, modality-agnostic architectures capable of withstanding sophisticated manipulations. This review synthesizes strengths and shortcomings of contemporary deepfake detection while charting paths toward robust trustworthy systems.
Diverse LLMs or Diverse Question Interpretations? That is the Ensembling Question
Venue: arXiv cs.CL 2025
Year: 2025

Effectively leveraging diversity has been shown to improve performance for various machine learning models, including large language models (LLMs). However, determining the most effective way of using diversity remains a challenge. In this work, we compare two diversity approaches for answering binary questions using LLMs: model diversity, which relies on multiple models answering the same question, and question interpretation diversity, which relies on using the same model to answer the same question framed in different ways. For both cases, we apply majority voting as the ensemble consensus heuristic to determine the final answer. Our experiments on boolq, strategyqa, and pubmedqa show that question interpretation diversity consistently leads to better ensemble accuracy compared to model diversity. Furthermore, our analysis of GPT and LLaMa shows that model diversity typically produces results between the best and the worst ensemble members without clear improvement.
Digitalizing Uncertain Information
Venue: arXiv cs.DB 2025
Year: 2025

The paper sketches some initial results from an ongoing project to develop an ontology-based digital form for representing uncertain information. We frame this work as a journey from lower to higher levels of digital maturity across a technology divide. The paper first sets a baseline by describing the basic challenges any project dealing with digital uncertainty faces. It then describes how the project is facing them. It shows firstly how an extensional ontology (such as the BORO Foundational Ontology or the Information Exchange Standard) can be extended with a Lewisian counterpart approach to formalizing uncertainty that is adapted to computing. And then it shows how this is expressive enough to handle the challenges. Keywords: actuality, BORO Foundational Ontology, counterpart, Information Exchange Standard, informational uncertainty, my doxastic actualities, two-dimensional semantics.
Contrast-CAT: Contrasting Activations for Enhanced Interpretability in Transformer-based Text Classifiers
Venue: arXiv cs.CL 2025
Year: 2025

Transformers have profoundly influenced AI research, but explaining their decisions remains challenging -- even for relatively simpler tasks such as classification -- which hinders trust and safe deployment in real-world applications. Although activation-based attribution methods effectively explain transformer-based text classification models, our findings reveal that these methods can be undermined by class-irrelevant features within activations, leading to less reliable interpretations. To address this limitation, we propose Contrast-CAT, a novel activation contrast-based attribution method that refines token-level attributions by filtering out class-irrelevant features. By contrasting the activations of an input sequence with reference activations, Contrast-CAT generates clearer and more faithful attribution maps. Experimental results across various datasets and models confirm that Contrast-CAT consistently outperforms state-of-the-art methods. Notably, under the MoRF setting, it achieves average improvements of x1.30 in AOPC and x2.25 in LOdds over the most competing methods, demonstrating its effectiveness in enhancing interpretability for transformer-based text classification.
Half-life of Youtube News Videos: Diffusion Dynamics and Predictive Factors
Venue: arXiv cs.SI 2025
Year: 2025

Consumption of YouTube news videos significantly shapes public opinion and political narratives. While prior works have studied the longitudinal dissemination dynamics of YouTube News videos across extended periods, limited attention has been paid to the short-term trends. In this paper, we investigate the early-stage diffusion patterns and dispersion rate of news videos on YouTube, focusing on the first 24 hours. To this end, we introduce and analyze a rich dataset of over 50,000 videos across 75 countries and six continents. We provide the first quantitative evaluation of the 24-hour half-life of YouTube news videos as well as identify their distinct diffusion patterns. According to the findings, the average 24-hour half-life is approximately 7 hours, with substantial variance both within and across countries, ranging from as short as 2 hours to as long as 15 hours. Additionally, we explore the problem of predicting the latency of news videos' 24-hour half-lives. Leveraging the presented datasets, we train and contrast the performance of 6 different models based on statistical as well as Deep Learning techniques. The difference in prediction results across the models is traced and analyzed. Lastly, we investigate the importance of video- and channel-related predictors through Explainable AI (XAI) techniques. The dataset, analysis codebase and the trained models are released at http://bit.ly/3ILvTLU to facilitate further research in this area.